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 Duration 21 hours

Course Outline

Introduction to AI-Augmented SQL

  • Overview of AI integration within data systems.
  • The shift from traditional SQL to AI-assisted querying.
  • Key enterprise use cases and associated benefits.

Understanding LLMs in SQL Context

  • How LLMs interpret and generate structured queries.
  • Comparative analysis of GPT, LlaMA, DeepSeek, Qwen, and Mistral for SQL applications.
  • Fine-tuning models for effective database interaction.

Natural Language to SQL (NL2SQL) Systems

  • Architectures and methodologies for NL2SQL.
  • Building and deploying text-to-SQL pipelines.
  • Evaluating query accuracy and understanding user intent.

AI-Assisted Query Optimization

  • Utilizing AI to identify and correct inefficient queries.
  • LLM-based query rewriting to enhance performance.
  • Integrating AI optimization into PostgreSQL and SQL Server.

Security, Governance, and Auditability

  • Managing access controls for AI-generated queries.
  • Ensuring explainability and regulatory compliance.
  • Implementing AI governance within enterprise data systems.

LLM Integration and Orchestration

  • Connecting SQL engines with AI APIs.
  • Utilizing frameworks such as LangChain and LlamaIndex.
  • Deploying AI components across hybrid and cloud architectures.

Practical Implementation Labs

  • Establishing AI-SQL connections and test environments.
  • Creating and evaluating AI-generated queries.
  • Measuring performance gains through AI optimization.

Future Trends and Enterprise Adoption Strategies

  • The rise of AI-native database systems and SQL evolution.
  • Integration with data lakes, BI tools, and data pipelines.
  • Building internal AI query assistants for organizations.

Requirements

  • A solid grasp of SQL fundamentals.
  • Practical experience in database administration or data engineering.
  • Foundational knowledge of AI or machine learning concepts.

Target Audience

  • Data engineers and database administrators.
  • Enterprise architects and analytics leads.
  • AI integration and platform engineering teams.

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